Systems and methods for communication routing

ABSTRACT

Apparatus and methods consistent with the present disclosure route electronic communications to an appropriate resource that can efficiently and effectively provide responses to inquires included in or that are associated with a particular electronic communication. Methods and apparatus consistent with the present disclosure may be optimized for various different types of communication mediums with different sets of capabilities, requirements, or constraints by evaluating data that may be associated with historical information or with a stream of information.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. Provisional Patent ApplicationNo. 62/588,425, filed Nov. 19, 2017 and U.S. Provisional PatentApplication No. 62/588,266, filed Nov. 17, 2017, the disclosures ofwhich are incorporated herein by reference in their entireties.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention is generally directed to systems and methods forrouting client communications. More specifically the present disclosureis directed to providing information to clients efficiently as possible.

Description of the Related Art

Customer call centers receive calls each day from customers, clients, orinterested parties that have a need or a concern that need be addressed.Call centers receive calls regarding various different subjects. Suchsubjects may, for example, be related to understanding productinformation, acquiring information, or to have a question answered. Nomatter what subject, each call center is concerned with cost effectivelyproviding excellent customer service by answering questions or providingrelevant information to callers quickly and politely.

Large companies, organizations, or enterprises are increasinglyconcerned with maintaining or improving customer satisfaction by beingable to quickly and efficiently service their customer needs when suchneeds arise. Strategically, the organization with the best customersatisfaction will more likely retain customers for the long term. Today,call centers not only receive telephone calls from customers or clients,they also receive communications via other forms of communications, suchas instant messages of various sorts (text messages, short messageservice (SMS) messages, ‘chat’ messages), and electronic mail (email)for example. In such instances, communications may be routed to eitherautomated assistants or to live persons when a customer or clientmessage is received.

While attempting to provide users with efficient and effectiveresponses, many call centers fail to meet or exceed user expectations.In certain instances, this may be because an automated assistant is notoptimized or capable of providing a response that can satisfy a user.Alternatively or additionally, even live persons communicating with anindividual may be unable to provide a customer with responses that meetthat customer's needs for reasons that may include fatigue or lack ofspecific knowledge.

What are needed are new ways to identify how best to forward and processuser communications in quick and efficient ways that meet or exceed userexpectations.

SUMMARY OF THE PRESENTLY CLAIMED INVENTION

The presently claimed invention relates to a method, a non-transitorycomputer readable storage medium, or a system executing functionsconsistent with the present disclosure. A method consistent with thepresent disclosure may receive requests from computing devices via firsttype of communication channel, receive information included incommunications with the requestor computing device, calculate a score,identify that score met a threshold associated with exhaustionthreshold, and initiate a corrective action after identifying that thescore met the exhaustion threshold.

When the presently claimed invention is implemented as a non-transitorycomputer readable storage medium, a processor executing instructions outof a memory may also receive requests from computing devices via a firsttype of communication channel, receive information included incommunications with the requestor computing device, calculate a score,identify that score met a threshold associated with exhaustionthreshold, and initiate a corrective action after identifying that thescore met the exhaustion threshold.

An apparatus consistent with the present disclosure may include aprocessor that executes instruction out of a memory when implementingmethods consistent with the present disclosure. Here again the methodmay include receiving requests from computing devices via first type ofcommunication channel, receiving information included in communicationswith the requestor computing device, calculating a score, identifyingthat score met a threshold associated with exhaustion threshold, andinitiating a corrective action after identifying that the score met theexhaustion threshold.

The presently claimed invention relates to technologies (e.g., a method,a non-transitory computer readable storage medium, and/or apparatus)that may include receiving a request from a computing device associatedwith a requestor that is associated with a first type of communicationchannel. In some embodiments, the technologies may include receivinginformation included in a set of communications with the requestorcomputing device; calculating an emotional exhaustion score, thecalculation based on the received information included in the set ofcommunications. In some embodiments, the technologies may includeidentifying that the emotional exhaustion score has at least met anemotional exhaustion threshold. In some embodiments, the technologiesmay include initiating a corrective action based on the emotionalexhaustion score at least meeting the exhaustions threshold.

In some embodiments, a corrective action may include routingcommunications from the requestor user device to a computing deviceassociated with a human agent. In some embodiments, the technologies mayinclude collecting communication information associated withcommunications between the requestor computing device and the humanagent computing device.

In some embodiments, the technologies may include calculating anemotional exhaustion score associated with the human agent.

In some embodiments, the technologies may include identifying that thehuman agent emotional exhaustion score has crossed a thresholdassociated with the human agent.

In some embodiments, the technologies may include sending advicemessages to the human agent computing device.

In some embodiments, the technologies may include identifying that theperformance of the human agent is consistent with an event in a rewardtrigger list. In some embodiments, the human agent may be provided withthe reward based on the identification that the performance of the humanagent is consistent with the event.

In some embodiments, the technologies may include identifying that theperformance of the human agent is consistent with a correlationthreshold related to a human performance factor. In some embodiments,the technologies may include storing information associated with theperformance of the human agent in a database based on the identificationthat the performance of the human agent is consistent with thecorrelation threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example digital data processing contact centerenvironment consistent with the present disclosure.

FIG. 2 illustrates an exemplary set of steps that may be performed byapparatus consistent with the present disclosure.

FIG. 3 illustrates an exemplary augmented or artificial intelligentsystem (AI) system consistent with the present disclosure.

FIG. 4 illustrates a communication network that may be used to sendcommunications between different computing devices when methodsconsistent with the present disclosure are performed

FIG. 5 illustrates exemplary computing devices that may be used toimplement methods consistent with the present disclosure.

FIG. 6 illustrates a set of exemplary steps that may be performed bylearning module software consistent with the present disclosure.

FIG. 7 shows example graphs of data that may be compiled and used by amachine learning module consistent with the present disclosure.

FIG. 8 includes a table of data that associates data associated withdifferent agents with parametric data collected over a span of time.

FIG. 9 shows an example graph of a trend analysis showing the number ofaggression levels or acts by time.

FIG. 10 illustrates an exemplary chart of program flow that may beimplemented to calculate rewards that may be provided to an agentworking at a call center.

DETAILED DESCRIPTION

Apparatus and methods consistent with the present disclosure routeelectronic communications to an appropriate resource that canefficiently and effectively provide responses to inquires included in orthat are associated with a particular electronic communication. Methodsand apparatus consistent with the present disclosure may be optimizedfor various different types of communication mediums with different setsof capabilities, requirements, or constraints by evaluating data thatmay be associated with historical information or with a stream ofinformation.

This specification describes example systems and processes that routeelectronic communications. For example, the technologies describedherein may be used to connect a user of a contact center to a contactcenter agent. An example system may comprise an intake processing systemto process an incoming communication request, an augmented intelligencesystem (AIS) comprising an analytics engine to analyze certaincommunication data and to compute certain metrics, and a routing engine,which may use the computed metrics to connect the user, for example, toa contact center agent.

An example computer-implemented method for routing a communicationrequest initiated by a requestor, comprises receiving, by a processor ofan augmented intelligence system over a network, a transmission requestfrom a routing engine by a requestor from a routing engine to provide anemotional exhaustion metric for each of one or more human agents in acontact center that are available to process the communication requestfrom the requestor; receiving, by the processor of augmentedintelligence system, at least one of streaming data and historical dataassociated with any of the one or more human agents, the communicationrequest and the requestor, operating, by the processor of augmentedintelligence system, a data analytics engine to calculate the metric foreach agent based on any of the received streaming data and historicaldata, wherein, the data analytics engine selects and applies anemotional exhaustion model to infer a probability of each of the humanagents being emotionally exhausted over a specific period of time; andtransmitting, by the processor of augmented intelligence system, to therouting engine the emotional exhaustion metric for each of the one ormore human agents in the contact center wherein the routing engine usesthat information to route the communication request to any one of (i)one of the human agents, (ii) an automated interaction component, and(iii) a self-service capability configured to process the communicationrequest. In some embodiments, the routing engine routes thecommunication request to one of the human agents, where data may bestreamed to one or more human agents processing the communicationrequest. Data collected from a communication session may be transmittedto and augmented intelligence system where it may be processed by a dataanalytics engine when san emotional exhaustion metric for human agentsor requestors may be identified based on the streaming data.

While customer support has traditionally been provided widely viatelephone calls, there is a growing demand for servicing customer oruser requests using all types of available media, including telephonecalls, instant messages of various sorts (text messages, short messageservice (SMS) messages, ‘chat’ messages), audio/visual communicationmedia, web interface communication media, social media communications,exchanging of avatars and emoticons, and electronic mail (email). Thesevarious forms of media can be separated into categories, where eachparticular category is associated with one or more features orconstraints. For example, conventional telephone calls are limited toaudio communications as they do not include a capability of sharinginformation visually. Communications that use text messaging may belimited to text, with little or no provision for being able to sharepictorial or visual data. Other forms of media have the ability to shareinformation visually or may provide users with the ability to interactthrough a web interface. As such, each form of media or communicationtype, may be managed using metrics or protocols that consider featuresand constraints associated with a particular type of communication mediaor type of communication channel.

Increasingly, centers that received large amounts of customer inquiriesrely on forms of augmented or artificial intelligence (AE) systems thatare commonly used as a front line that attempts to resolve customerissues without need for human interaction. Of course, manycommunications received by such centers (commonly referred to as callcenters) still require a real live person or agent to resolve customerissues.

Methods and apparatus consistent with the present disclosure aredirected at improving the routing of communications to appropriateresources, whether those resources include augmented/artificialintelligence (AI) system or live personal agents that are tuned to thefeatures and constraints associated with any particular type ofcommunication channel or media. Such methods may identify, track, orcompute metrics that may be used to more efficiently route usercommunications to a resource that can best resolve issues efficiently ina world that includes multiple different types of communicationchannels/media types.

AI systems consistent with the present disclosure may include aprocessor that executes instructions out of a memory when informationincluded in or associated with a communication is evaluated. Such an AIsystem may identify or compute metrics that can be used to routecommunications to particular resources, this may be based at least inpart on a media/channel classification associated with a communicationtype. Each form of communication media or channel may be associated witha media classification. An audio only communication channel may belimited to audio only communications and a text messaging communicationchannel may be limited to written communications, for example. Otherforms of communication media types/channels may be more versatile, forexample an audio/visual communications channel and a web interfacecommunication channel can potentially use both audio and visualcommunications when identifying information included in or associatedwith a particular communication request.

In certain instances data collected over time or data received in acommunication stream may be evaluated by a form or artificialintelligence or AI system when an emotional exhaustion or stress metricsare identified. After such emotional exhaustion metrics are identified,they may be used in calculations to determine whether a level of stressor emotional exhaustion of a customer or of a live agent has met orexceeded a threshold level. Methods consistent with the presentdisclosure may be implemented in a computer server or in a cloud-basedcomputing system that may collect information from subscribed users.Information processed by such AI systems may include batch processing ofdata collected in real-time or in near-real-time. Information collectedand evaluated continuously over short time spans (e.g. withinmilliseconds or seconds) may be referred to as real-time data andinformation that is collected and evaluated over longer time spans (e.g.within minutes) may be referred to as near-real time data.

Streaming Data is data that is generated continuously from one or moresources. Examples of streaming data include audio signals, mobile sensordata, and/or a sequence of mouse clicks/clickstreams that may have beenreceived from by one or more sources. Such sources may transfer datarecords simultaneously to the Augmented Intelligence System. The datamay be processed a number of ways, for instance it could be processed(1) incrementally using stream processing techniques without havingaccess to all of the data or (2) processed at discrete time frames (e.g.after a caller stops talking) or (3) processed by a trigger event (e.g.volume on a call goes up). There are numerous ways to process thestreaming data. In addition, it is recognized that “drift” may happen inthe data which means that the properties of the stream may change overtime. Drift can be used to trigger other events, such as usingcorrections algorithms. The result of streaming data is essentiallyprocessing big data in which it is generated by many different sourcesat high speed.

Data streaming can also be explained as a technology used to delivercontent to devices in a call center, between call center agents and callcenter managers, between callers and agents and between suppliers tocall centers. Data streaming allows users to access the contentimmediately, rather than having to wait for it to be downloaded. Datastreaming also allows for reduction on data storage costs. Datastreaming using real-time analysis is received data may be used toidentify metrics relating to the resolution of a customer issue, forexample.

Metrics associated with stress or emotional exhaustion may also be usedto forecast or predict other metrics that may relate to a measure ofcustomer satisfaction, a level of exhaustion of a live person thatresponds to customer calls, and probabilities that may be associatedwith average call handling times or with a likelihood that a particularagent can resolve an issue quickly.

FIG. 1 illustrates an example digital data processing contact center ora call center environment consistent with the present disclosure. FIG. 1includes a contact center digital data processing system 100 comprisingan intake processing system (IPS) 140, an augmented intelligence system(AIS) 150, a routing engine 160, one or more automated responsetechnologies 130, self-service capabilities 120 and human agents 110.Human agents 110 may be equipped with phone 115A or with computer 115Bthat they may use process the communication requests routed to them.

As illustrated, a requestor (e.g., a customer, prospective customer,member etc.) 180 may send a communication request to the contact centerover a network through a channel (e.g., voice, fax, SMS, web etc.) 190using a communication device (not illustrated in FIG. 1) that may be acomputing device (e.g., laptop, computer, tablet), mobile device,telephone, computer, voice over internet protocol (VoIP) phone, or thelike, that may be associated with the individual. One or more publicnetworks (e.g., a public switch telephone network or PSTN or theInternet) 170, local area networks (LANs), wide area networks (WANs),metropolitan area networks (MANs), closed or private networks or anyother similar communication network may be used when methods consistentwith the present disclosure are implemented.

Incoming communication requests from requestors 180 may be received atcontact center 100 may be routed by a routing engine 160 after beingreceived by intake processing system (IPS) 140. IPS 140 may include oneor more pieces of computer hardware and software components. In certaininstances, hardware and software associated with IPS 140 may beconfigured to perform operations with certain types of communicationmedia or channels. For example, requestor 180 may initiate acommunication request with an organization through a phone call over apublic switched telephone network (PSTN) via a Private Branch Exchange(PBX) when preparing to communicate with contact center 100. Such a PBXmay include a call answer unit and an interactive voice response unit(IVR) that permits the requestor to select options in response toprompts using a touchtone keypad or voice, for example. The IVR may becommunicatively coupled with a database that contains data aboutcustomers of an organization, this information may include accountinformation and customer historical information. Based on the optionsselected by the requestor and/or data associated with a communicationrequest (CRM) data, the IVR may identify that requestor 180 is anexisting customer that is initiating a new communication request. Insuch an instance, data received from the customer may be used toidentify that customer 180 has a goal of changing their home address ontheir account. The IVR may then pass this customer information and theassociated goal to any one of a contact center agent 110, a self-servicecapability 120, or to an automated response entity 130. An IVR maygenerate voice responses using a voice response unit (VRU), construct aresponse that may be sent by email, or may communicate with requestor180 by other means based on decisions that may be made by routing engine160. As such, routing engine 160 may act as an automatic calldistributor (ACD) or a business rules engine (BRE)). The routing engine160 may make communication routing decisions based on decision logicthat that makes decisions based on one or more metrics, pieces ofinformation, or goals associated with particular communication requests.

In some embodiments, a routing engine 160 can identify and connect auser to an appropriate agent using one or more techniques, that maydirectly route message to resources (e.g. artificial intelligence orhuman) that is best suited to address a particular communicationrequest. Techniques used to route calls may be associated with differenttypes of protocols that may be called direct routing, least-idlerouting, least-occupied routing, skills based routing, dynamic routing,or business rules routing. Direct routing may include requestor 180directly dialing a particular live agent, least-idle routing may includetransferring a communication to an agent that has been idle a longestperiod of time, least-occupied routing may include transferring thecommunication to an underutilized live agent, skills-based routing mayinclude routing the communication to a live agent with a highest skilllevel, dynamic routing may include routing the communication to back-upagents, and business rules routing may include routing communicationsfrom premium customers to a live agent with a highest level of skill.

In some instances, a routing engine 160 may receive information from theAIS 150 that can be used to route a communication request from therequestor 180 based upon historical and/or real-time emotionalexhaustion metrics. The AIS 150 may communicate historical and/orcurrent emotional exhaustion metrics for one or more agents in a contactcenter to the routing engine 130 which may, in turn, use that datacombined with one or more factors to optimize routing, workforceutilization, or other key performance indicators. For example, routingengine 160 may recommend agents 110 which have previously taken a callfrom a particular requestor. Alternatively routing engine 160 may routea communication to agents with experience handling requestors 180 withsimilar emotional exhaustion metrics. Once a communication request isforwarded to AIS 150 via the IPS 140, the requestor's historical datamay be historical data of available agents. An agent with a high successrate with similar requestors and/or communication requests may beselected when associated emotional exhaustion metrics meet certainthresholds. After a particular agent is selected, the communicationrequest may then be transferred to the selected agent.

In certain instances, a requestor device may interface with the contactcenter PBX using a session initiated protocol (SIP). The SIP is acommunications protocol for controlling multimedia (e.g., audio dialog)sessions such as internet telephony, voice and video calls, and thelike. Also, some embodiments may be extended to WebRTC which allows formulti-channel interactions e.g., voice, chat and video etc.

In other embodiments, a requestor may initiate a communication requestwith an organization through the web channel over the Internet 170 byaccessing the organization's website on their computer and initiating achat session with a chat bot or another automated chat assistant. Inthis scenario, the intake processing system 140 may include a web serverthat hosts an organizational website along with operating one or moreother technologies for chat, text analytics and search capabilities.Also, such a web server may be communicatively coupled to a CRM databaseor other data store that stores customer data, customer prospects, orother individuals who may have previously interacted with theorganizational website. Based on the user's initial selection of chatbot as the preferred channel of communication, the IPS may pass thatselection information along with other data (e.g., browser sessioninformation) associated with communication request through to therouting engine 160. The routing engine 160 may then route the request tothe automate response chat bot technology 130 based on the requestorselection, where to begin interacting with the requestor. As therequestor enters more information about themselves and their goal(s) ina chat session, a text analytics component may be used to parse theinputs typed by a requestor. This information typed by the requestor maybe combined with the one more other data. For example, typed requestorinformation may be combined with browser session information that may beused to identify the requestor, an account associated with therequestor, an interaction history, or and goals associated with acommunication when a CRM database is searched for relevant information.During the requestor's chat interaction, the requestor may initiate arequest to be transferred to a human agent from the automated chatassistant. At that point, the IPS 140 may pass along requestor, goal,and other information associated with the communication request torouting engine 160. Routing engine 160 may then, route the communicationrequest to an agent using decision logic that uses that communicationrequest information as input.

Besides the aforementioned web and voice channel examples, the IPS maycombine one or more other hardware and/or software components that varydepending upon the other channels, devices and/or networks associatedwith the communication request. For example, written communicationrequests sent through, SMS, email and/or fax may involve the use of SMSapplications/gateways, email and/or fax servers, respectively, that mayalso deploy or operate with natural language processing (NLP) and/ornatural language understanding (NLU) technologies to parse and/oranalyze the written text and identify the requestor and/or goalsassociated with the request. Still other combinations of hardware and/orsoftware technologies are possible for the IPS 140.

An embodiment of the invention may be directed to improvingcommunication routing and performance in contact centers by interfacingor otherwise communicatively coupling an Augmented Intelligence System150 (AIS) with the IPS 140, Routing Engine 160, and/or the one or morehardware and software components that comprise self-service 120capabilities of data processing system 100. Automated responsetechnologies and/or systems or devices used by the human agents 110 mayalso be received communications from routing engine 160. The routing ofcommunications may be accomplished by transmitting information about therequestor and other communication request information to the AIS 150.AIS 150 may include one or more data analytics engines that process orotherwise utilize transmitted information along with other historicaland/or streaming data to compute emotional exhaustion metrics for therequestor and/or the one or more human agents in the contact center.Those metrics may then transmitted by the AIS 150 to the routing engine160 which may use these metrics as data inputs for making routingdecisions. Such decisions may be made by logic that optimally matchesthe requestor's communication request with one of the human agents 110,self-service capability 120, or automated response technologies 130.Furthermore, any metrics associated with a particular request may bepassed to whichever request processing resource (110, 120 or 130) thatis selected to receive a particular communication request. Once thecommunication request is routed and a communication connection with therequestor is established, any metrics associated with the requestor canbe used during that interaction to provide additional context for thecommunication request. In addition, data associated with suchinteractions may be transmitted back to AIS 150 to update the emotionalexhaustion metrics or other metrics associated with requestor 180,agents 110 in real-time or in near-real time. In certain instances,information associated with a communication request may be processed aspart of a batch process.

The data analytics engine may perform a combined analysis of the wordsand/or text associated with a communication request as well we as thecorresponding behavioral metrics associated with that same request asinputs into an emotional exhaustion model that computes an emotionalexhaustion metric for a user (e.g., a requestor or an agent). Forexample, the phrase “No” could be transcribed or otherwise recognized,and also the pitch determined to be “high” along with the loudness suchthat it may be inferred that the user is emotionally exhausted. On theother hand, the same word “no” could be transcribed or otherwiserecognized but the pitch is normal, timing is normal and loudness isbelow a certain threshold indicating that the user is not emotionallyexhausted. It should be noted that certain ‘negative’ words could be anindication that an emotional exhaustion level has been exceeded.

In certain instances, routing engine 160 may receive information fromthe AIS 150 that may be used to route a communication request from therequestor based upon historical and/or real-time emotional exhaustionmetrics. The AIS 150 may communicate historical and/or current emotionalexhaustion metrics for one or more agents in a contact center to therouting engine 160 which may, in turn, use that data combined with oneor more factors to optimize routing, workforce utilization and other keyperformance indicators. For example, routing engine 160 may recommendagents which have previously taken the requestors call, agents withexperience handling requestors with similar emotional exhaustionmetrics, etc. Once the requestor is forwarded to the AIS 150 via the IPS140 the requestor's historical data can be compared with the historicaldata of available agents. An agent with a high success rate with similarrequestors and/or communication requests may be selected if theemotional exhaustion metrics meet certain thresholds and the requestoris transferred to the selected agent.

In an instance where an AI system initially receives an audio onlycommunication request, that AI system may first receive verbalinformation from a person that identifies that they have an issue withan appliance in their house that they wish to resolve, for example. AnAI system receiving such a call may communicate with the caller byproviding a series of questions and by receiving answers to thosequestions from the caller. Such an AI system may provide these questionsusing a human voice and receive answers via an audio communicationchannel where a communication system at the AI system identifies callerresponses using voice recognition techniques known in the art. As such,the AI system/caller interaction may include the following dialogue thatis an exemplary communication using a voice only communication channel.The following is an example for a Voice Only Communication Channel:

-   -   AI System: “What issue can I help you with?”    -   Caller: “My refrigerator is not cold.”    -   AI System: “What is the make and model of your refrigerator?”    -   Caller: “My refrigerator is a Kenmore 789125.”    -   AI System: “Does a light com on when you open the door?”    -   Caller: “No.”    -   AI System: “Please check your refrigerators electrical plug. Is        plugged in? If no, please plug it in as see if the light turns        on when you open the door and then let me know.”    -   Caller: “Thank You, yes it works now!”    -   AI System: “Nice working with you. Do you have another question        or concern?”    -   Caller: “No.”    -   AI System: “Great, thank you for calling, bye.”

Note that the voice only communication channel example above allows theAI system to converge to a solution after the AI system performs voicerecognition on at least the words in italics above. In such a process,the AI system after identifying certain words or phrases in response toa first question, may identify a second question based on a routingassociated with the AI system, where subsequent questions or commentsfrom the AI system vary based on responses received from a caller. Assuch, an AI system may alternatively, in an instance where the callerprovided a different set of answers, identify that the caller'srefrigerator was not cold because a refrigerator setting was not setproperly and may instruct the caller to change that setting whenconverging to a solution. Each of the answers received by an AI systemmay correspond to one or more metrics that the AI system may use tocalculate thresholds, probabilities, or other metrics that may be usedin routing decisions. In an instance where the AI system does notconverge to a solution within a certain amount of time, within a certainnumber of questions, or when the AI system has exhausted all relevantresolution routes, that AI system may route the caller to a live humanagent for further action.

Alternatively or additionally, an AI system may identify metricsassociated with the voice of the caller, were certain types of comments,intonations, or tones uttered by the caller when identifying whether thecaller was stressed or may be suffering from emotional exhaustion. In aninstance where the AI system identifies that a caller is emotionallyexhausted or is stressed above a threshold level, the AI system mayinform the caller that they are being transferred to a “personal expertthat can best help them resolve their issue.” As such, communicationmediums that use audio may be sensitive to not just types of wordsspoke, yet may also be sensitive to tones utter by a caller or towhether certain words are spoken. For example, a change in pitch from alower frequency to higher frequency, an increase in pitch, or afluttering in the voice of a caller may be associated with an emotionalexhaustion or stress threshold or a stressor pattern. If a number ofsuch stressor events increases as a call progresses may, for example,may cause the AI system to forward the call to a live personal agent.

Metrics associated with voice inputs may be used to identify emotionalexhaustion of an agent or a customer. Such voice inputs may be analyzedto identify current voice behavioral data that may be compared tohistorical trends. Such voice metrics may be related to a pitch, a tone,a spoken pace/pace change, or a vocal effort. This process may includeidentifying an increase in emotional labor of a person included in acommunication session. For example, that fact that agent is continuallyreadjusting and coordinating their effort to deal with a caller byremaining calm, actively listening, increasing patience, or showingempathy may be used to identify an emotional state or metric associatedwith that agent. Another identification that may be made using vocalqueues may include an agent speaking too quickly may indicate that theagent is under time pressure or is concerned that metrics associatedwith their productivity could negatively impact their compensation. Inother instances, words spoken by a customer currently may be compared towords spoken by that customer in previous communication sessions. Assuch, historical data combined with information related to a currentcommunication session, may be used to identify whether a customer orhuman agent is excessively stressed or is emotionally exhausted. Suchdata may be used to identify that a customer is currently unusuallyaggressive or demanding or may be used to identify that an agent issuffering from a lack of autonomy or enthusiasm when they user more thana threshold number of scripted words. This data may also be used to makeother determinations or calculations that may relate to identifyingincentives, rewards, or performance metrics to associate with aparticular agent.

A communication channel that relies on text only communications mayproceed in a manner that is very similar to the audio only communicationchannel reviewed above. Here, however, an AI system receiving such acommunication could only rely on textual information when making routingdeterminations. As such, metrics relating to a requestor stress orexhaustion level may be limited to the type of words used and not tonalor other information associated with the anomalies in the utterances ofa person.

Communication channels that use a visual user or web interface mayinclude communication routes that have a different look and feel thanother types of communication channels. For example, a user interface maypresent information in parallel to a requestor that includes fields thatcan be selected by the requestor. In such an instance the requestor maymake selections that identify that their Kenmore® 789125 refrigerator isnot cold and then an AIs system may update the user interface to includeinitial comments and sub-windows that can be reviewed by the requestor.Here, an initial comment provided via the user interface may state, “Ifyour refrigerator light does not come on when the door is open, pleaseselect an appropriate sub-menu to see instructions regarding quicklyresolving your refrigerator issue.” These sub-menus may include a firstsub-menu that includes instruction on how to check the power cord, asecond sub-menu that identifies how to check an on/off switch, and athird sub-menu that instructs the requestor to test a powersocket/outlet that the refrigerator is plugged into. As such,communication mediums that use a web or user interface can providefeatures and benefits that are not natural to audio or text onlycommunication medium. This in part may be because a requestor mayalready know that the refrigerator is plugged into a working poweroutlet, thereby, allowing the requestor to skip one or more steps may berequired when another type of communication channel is used.Communication channels that use a web or user interface may also beassociated with different metrics that can be used to identify a stresslevel or a level of emotional exhaustion of a requestor, for example, inan instance where a particular requestor clicks through many differentsub-menus or when that requestor reputedly selects a particularsub-menu, an identification can be made that the requestor stress oremotional exhaustion level is above a threshold level and thatcommunication can be passed to a live personal agent for resolutionbased.

Email may also be associated with different features and constraints. Afeature associated with email is that attachment documents and web linksmay be provided with them. Of course, attachments and web links may alsobe associated with risks that include potentially being exposed to acomputer virus. Limitations associated with email include time delay,communications are more likely to be routed to a junk email box wherethey may not be read, and they provide limited or no sense that requestsare being reviewed in real or near-real time. Of course, instructionsprovided in an email may inform a requestor of other communicationmechanisms that may be used by the requestor that may provide moretimely responses to their requests.

When audio and video communication channels are used, requestors mayexperience features and benefits that include metrics associated witheach of audio, text, and web/user interface communications. This isbecause an audio/video channel may provide access to vocal cues providedby a requestor and may provide selections that may be reviewed via adisplay interface. Communication channels that combine both audio andvideo may, therefore, benefit from new features, such as being able toanswer a question provided visually by speaking. Here again, stresslevel of emotional exhaustion levels may be identified by usingtechniques used with both audio channels and with web/user interfacecommunication channels discussed above.

While stress or emotional exhaustion metrics may be incorporated intomethods and apparatus consistent with the present disclosure, othermetrics that may be used include informational metrics, effort metrics,customer satisfaction metrics, performance metrics, or productivitymetrics, for example. Information metrics may identify a make or modelof a product, may identify a problem, or identify a location where aparticular issue is being experienced. Effort metrics may be used totrack how much effort either a requestor is investing into identifying asolution or may be used to track how much effort is being spent by aresponse system when attempting to help a requestor to find anappropriate response to their request. Productivity and performancemetrics may be used to track the success or failure of specific humanagents that work at a contact center.

The various metrics described, herein, may be used to calculate othermetrics when a support system routes certain specific communications toparticular resources dedicated to servicing requestors. A requestoreffort metric may be incremented each time a requestor answers aquestion or selects a sub-menu, for example. A system effort metric maybe incremented each time an automated assistant or a live agent providesinformation or questions to a requestor. In such instances, such arequestor effort metric or system effort metric may be included in acalculation of an overall stress score that may include other metrics,like those discussed above. Such an overall stress score, when itreaches a threshold level may be used to identify that the communicationshould be passed to a live agent. A live agent may be assigned thecommunication based on a score associated with that particular agent.Such agent scores may be calculated using a productivity metric, aperformance metric, a satisfaction metric, or another metric associatedwith that particular agent. The various scores or metrics discussedabove may be updated overtime, where historical data is stored in adatabase/memory that may be accessed by an AI system when communicationsare routed. This may allow the AI system to identify that a particularrequestor could best be serviced by a particular live agent based onhistorical data associated with either the requestor, with the agent, orboth.

Alternatively or additionally, stress or emotional exhaustion of anagent may be identified and when a level of stress or emotionalexhaustion is identified as having met a threshold, using calculationsassociated with received audio, text, or video information. Theprocessing of real-time video data may include calculating an emotionalexhaustion metric using facial recognition data and behavioral datagathered through the video stream. The video stream data may be sent tothe AIS 150 via the IPS 140 of FIG. 1 in order to compute an emotionalexhaustion metric level through data analytics encompassing the facialrecognition and behavioral data from the video stream combined with thevoice data in order to provide individuals (e.g., an agent and/or arequestor) with recommendations or insights that would include thefacial recognition and/or behavioral data. For example, video data thatmay include facial recognition and behavioral data, may be collectedprior to a time when a requestor communicates with an agent. This mayallow the AIS 150 to calculate an emotional exhaustion level prior to aninteraction between the requestor and the agent. This data may becontinued to be collected during such a requestor/agent interaction.Additionally the voice data may be used when an overall emotionalexhaustion metric is identified. As such a stress score or an emotionalexhaustion score may be based on facial recognition data, behavioraldata, and voice data. An analysis off of this information may be used toinform an agent of suggestions during the requestor/agent interactionsin real time. For example, the agent may receive recommendations thatinform the agent to speak at a slower pace, be more sincere, etc. Inaddition, the calculated metrics collected prior to the requestor/agentinteraction may be used by the AIS 150 to provide data analytics to theRouting Engine 160 to pair the requestor with a more compatible agentbased on the emotional exhaustion levels determined by the facialrecognition data and/or behavioral data.

IPS 140 of FIG. 1 may also detect the requestor's language and basedupon the language of the requestor use a specific emotional exhaustionmodel. The IPS 140 may detect the language and send an alert to the AIS150 to use the correct emotional exhaustion model when creating outputsfor the event. This would provide more accurate outputs since variationsin speech patterns (tone, pitch, pace, etc.) in different languages canlead to different conclusions. The language may be detected by the IPS140 by a numerical input from the requestor. For example, “for Englishpress 1, for Spanish press 2” etc. This step may occur when the IPS 140is identifying the requestor and/or associated goals prior to beingforwarded to the AIS 150.

FIG. 2 illustrates an exemplary set of steps that may be performed byapparatus consistent with the present disclosure. The steps illustratedin FIG. 2 may be performed by an intake processing system/server (IPS)like IPS 140 of FIG. 1. In a first step 210 of FIG. 2, a communicationrequest is received at a communication/call center and then in step 220,one or more identifications may be performed. For example, step 220 mayidentify the identity of a customer that provided the communicationrequest and one or more goals associated with the received communicationrequest.

After step 220, determination step 230 may identify whether thecommunication request was received from a known requestor. Such adetermination may be made using an identifier associated with a previouscustomer, for example. When determination step 230 identifies that thecommunication request has been received from a known requestor, programflow may move to step 240 where information related to that knownrequestor may be forwarded to an AI system consistent with the presentdisclosure.

After step 240 or after step 230 identifies that the communicationrequest is not from a known user, the program flow may move todetermination step 250. Determination step 250 may then identify anygoals that may be associated with or included in the communicationrequest. Determination step 250 may then identify whether any goals areassociated with the communication request; when not, program flow maymove to step 270, where the flow chart of FIG. 2 ends. Whendetermination step 250 identified one or more goals associated with thecommunication request, program flow may move to step 260, where goalinformation may be passed to an AI system consistent with the presentdisclosure. After step 260, the flow chart of FIG. 2 ends in step 270.While FIG. 2 illustrates the ending of program flow in step 270, AIsystems or routing engines consistent with the present disclosure mayperform additional steps where the AI system may communicate with therequestor or a routing engine may route the requestor's communication toa human agent.

In certain instances, an intake processing system/server (IPS) mayfollow the same process illustrated in FIG. 2 but may directly passalong the output of that process (e.g., requestor identity, one moregoals and/or other communication request data) to a routing enginerather than transmitting the output of the process to an automatedintelligence system (AIS). In such a scenario, the routing engine maythen transmit data (e.g., a query or data request along with thecommunication request data it received from the IPS) to the AI systemimmediately or after some time when it is ready to route thecommunication request. Upon receiving such a query, a data ortransmission request may be sent from the routing engine, after whichthe AI system may calculate the requested metrics and transmit theinformation to the routing engine such that the communication requestwill be sent to an appropriate resource for processing.

In an example, where an IPS interfaces with a routing engine through anAI system, a process utilizing the technologies described herein may beperformed. Here again the IPS may receive a communication request sentby a requestor (e.g. a customer) via a communication network. In someembodiments, the communication request may comprise a combinedaudio-video communication sent over the web. In such an instance, theIPS may processes the communication request as illustrated in FIG. 2.Output data may then be transmitted to an AI system for furtherprocessing. The AI system may process the data received from the IPSaccording to one or more models used to identify an emotional exhaustionmetric or an exhaustion/stress score of a customer. Alternatively oradditionally, the AI system may calculate a measure of emotionalexhaustion (e.g., a score) for each of one or more agents using the dataanalytics engine. Such calculations may be performed using historicaldata, streamed data, or other data associated with a communicationrequest. In some instances, emotional exhaustion metrics of the customerand/or each of the one or more agents can be transmitted to a routingengine. The routing engine may then use the emotional exhaustion metricsof the customer and/or agent as input variables for one or morecustomer-agent matching processes.

In another example, where an IPS interfaces with the routing enginedirectly, a process utilizing the technologies described herein mayinclude a communication request sent by a customer that is received bythe IPS via a communication network. In some embodiments, thecommunication request may comprise electronic text communication. TheIPS may then process the communication request as illustrated in FIG. 2,when the IPS generates output data. This generated output data may betransmitted to routing engine. In certain instances, such output datamay include information from a customer response database (e.g., from aCRM database). This output data may indicate that a particular requestedcommunication may be challenging for a handling agent. When this is thecase, the routing engine may communicate with AI system to requestmetrics relating to trends in emotional exhaustion of the requestor aswell as each of the one or more agents over the last one hour, whentrend information in reviewed within a last few minutes may indicatethat a stress or emotional exhaustion threshold has been crossed. Insuch instances, the AI system may transmit the requested data back tothe routing engine and the routing engine, in turn, can then use metricsrelated to the requestor or each of the one or more agents as inputvariables for one or more customer-agent matching processes.

Systems and techniques described herein can be used to improvecommunication performance and routing in multiple ways. As mentionedabove, it improves existing routing engine technology by enabling moreprecise matching of requestors' communication requests with humanagents. These systems and methods may also be used to provide automatedresponses via self-service capabilities of the data processing system100 of FIG. 1. In addition, since the requestor's emotional exhaustionand other related metrics can be computed and passed along with therouted request, these methods provide better contextual information toautomated systems or to agents such that customers can be served better.Such systems and methods may also allow contact centers to better managetheir work queues of communication requests while providing a healthierand more productive work environment for their agent staff by minimizingtheir work stress. This, in turn, may reduce agent churn/turnover ratesand may improve other contact center operational metrics, such ascustomer satisfaction scores.

FIG. 3 illustrates an exemplary augmented or artificial intelligentsystem (AI) system consistent with the present disclosure. FIG. 3includes an AI system 300 communicatively coupled to data streams 310,data store 330, and historical data archive 320. FIG. 3 also identifiesthat AI system 300 may be coupled to systems 340 and applicationsystems/programs 350 via network 370. Persons 360 may include eithercustomers or human agents that may interact with each other according tomethods consistent with the present disclosure.

Note that AI system 300 includes an application program interface (API)layer 300A, authentication software 300B, user interface 300C, reportingsoftware 300D, analytical software 300E, event processing software 300F,data processing capabilities 300G, models 300H, and otherhardware/software 300J. API layer 300A may provide a capability forusers 360 of systems 340 or application 350 to communicate with AIsystem 300 via user interface 300C. As mentioned is respect to thevarious figures of the present disclosure, AI system may include aprocessor that executes instructions out of a memory to authenticateusers (customers or agents), perform data processing tasks, computedata/analytics, make determinations regarding identified events, orgenerate reports according to one or more computer models. Alternativelyor additionally other computer hardware or software 300J may be used toperform calculations and determinations consistent with the presentdisclosure.

AI system 300 may receive data from and/or transmit data to an internalor external data storage device 330. An AIS 300 may receive and/orreceive streaming data 310 and/or historical or archived data 320. Insome embodiments, AIS 300 may be directly connected to a network, 370.

AI system 300 as described herein may include or may be connected to oneor more platform components or services that may include applicationprogramming interface (API) 300A that facilitates integration with otherapplications or services, a user interface 300C (that may be a graphicaluser interface (GUI)), machine learning and/or artificial intelligencecomponent, an output or reporting module 300D, authentication module300B, a data processing (Streaming and/or historical/archived data)module 300H, an encoding and/or computation module, a video/audio/textanalytics module, an event handling module, and/or a storage module 330.

In some embodiments, a AI system 300 can be used to optimizecommunication routing and performance, call center environment 100 ofFIG. 1. In some embodiments, AI system 300 comprises a data analyticsengine 300E that may calculate one or more output metrics from inputdata. As such AI system 300 may use processing capabilities to analyzeuser behavior when exhaustion metrics are identified.

An emotional exhaustion metric associated with agents and/or requestorscan be used as a data input for decision logic used in one or morerouting engines at a call center (e.g. at an Automated CallDistributors). Decision logic may also use one or more business ruleengines in a multi-channel contact center to better manage work queuesand route communication requests more efficiently across different typesof communication channels.

In certain instances, an emotional exhaustion metric associated with afirst agent may be combined with an emotional exhaustion metric of asecond agent to be used as a data input for decision logic used in arouting engine. In such an instance, the emotional exhaustion metric ofa first agent may be computed and/or stored by the AI system 300 andcombined with the emotional exhaustion metric of a second agent by theAI system 300 when a communication request from a customer is evaluatedin order to create emotional exhaustion metrics consistent with thepresent disclosure. The combined emotional exhaustion metric may be usedto provide a supervisor with a summary of information that may be usedto identify how the agent can provide better services to customers thanthe second agent. This information may then be used to instruct thesecond agent about how he can improve.

Emotional exhaustion metrics may also be used to forecast futureperformance of the contact center operation performance metrics,including but not limited to customer satisfaction (CSAT) score, acustomer and or agent churn/turnover rate, first call resolution (FCR)rate, an average handle time (AHT), or other metrics. In some instances,data derived from or associated with an agent is used to assessexhaustion, a feeling of being emotionally drained, or a lack of energy.Data derived from or associated with a requestor may be used by AIsystem 300 to assess exhaustion. Such operational metrics have specificlevels that need to be maintained by an agent. These maintenance levelsmay be accessible to the AIS 300 via data store 330 or historical dataarchive 320. In an instance where an average handling time of 10-20minutes per communication request is common and this average handle timethreshold is exceeded, then the emotional exhaustion metric may becomputed to determine whether stress or emotional exhaustion isaffecting the word of an agent. AI system 300 may then identify possiblesolutions to the agent's slow average handing time in real or near-realtime.

Emotional exhaustion metrics of an agent or requestor, may be comparedto similar historical emotional exhaustion data accessible to the AIsystem 300 when forecasts are made regarding the future performance ofan agent. This forecast or prediction may lead to certain interventionsor notifications to the agent. For example, the agent may be providedwith insights or recommendations via a user interface. Alternatively oradditionally audio or video feedback may be provided to an agent whenthat agent is tutored. This may help that agent achieve key performancetargets, keep a requestor's emotional exhaustion metric below a certainlevel, or to more rapidly achieve a goal associated with a requestor. Ansuch, AI system 300 can use data analytics from a current event orcommunication request and compare this data continuously againsthistorical emotional exhaustion metric data of similar events in orderto provide the agent with appropriate interventions or notifications inlight of the desired outcomes for the communication requests,operational targets for the contact center or other goals. As such, AIS300 may analyze historical emotional exhaustion metrics by predeterminedtime intervals and store emotional exhaustion metrics associated withcommunications between an agent and a requestor. This could allow theAIS 300 to store the changes in the emotional exhaustion metric based onthe previous communications between a requestor and agent. Informationrelated to the interventions or notifications can be linked to changesin the emotional exhaustion metric over time. Details associated with aparticular communication request may be used to identify that a similarevent is likely to occur in the future and the AIS 300 can identify acorrective action before a forecasted change in the emotional exhaustionmetric is realized. This may help provide agents with the ability toachieve one or more desired goals more easily. In some embodiments,optimization of contact center communication routing via automatic calldistribution that could be performed by the data analytics engine 300E.This may also be used to generate one or more models that may be able toinfer measures of customer effort based on a combination of metrics orfactors. Such combined metrics/factors may include metrics or factorsrelated to:

A. An amount of effort expended to date to try and accomplish one ormore goals associated with the communication request.B. A an amount of effort required to accomplish one or more goalsassociated with a communication request.C. A measure of emotional exhaustion measure for an agent at a specifiedperiod of time.D. A measure of emotional exhaustion of an agent or customer.

Measures of emotional exhaustion for an agent at a specified period oftime may be a function of incoming/outgoing traffic, measures of anamount of time related to a customer support issue. A first time basedmetric may be associated with the fact that a customer is currentlyparticipating in a live call with an agent. A second time based metricmay be associated with scheduling a future call with a customer or maybe related to a queue that stores customer request information. Measuresof emotional exhaustion for a customer may include a period of time thatthe customer has invested in a communication or may be related to a timewhen a particular communication was sent in the case of an emailcommunication.

Described herein are technologies related to electronic communicationrouting, e.g., using an Augmented Intelligence System (e.g., cloud-basedor on premise) that can consume streaming and/or historical/archiveddata to calculate and publish (through an API layer, for example) anEmotional Exhaustion metric for an individual who subscribed to thesystem or is otherwise associated with the system such that theindividual's data is accessible to the system for analysis. The systemcan support real-time, near-real time and batch processing use casesdepending upon how the output is being consumed by users, systems,and/or services that interact with the system.

In some embodiments, data may be received, processed and/or transmittedby AIS 300 using real-time processing. In some embodiments, real-timeprocessing may use continuous input, processing and output of data. Incertain instances, only response times in milliseconds or seconds may beacceptable. In such instances, real-time processing may be used inconnection with streaming data such as from device and mobile sensors,web clickstreams, social media, real-time audio and video.

Data may be processed by AIS 300 in near-real time. As such, speed maybe important but slightly delayed response times in minutes instead ofmilliseconds or seconds may be acceptable. Input data may be collectedimmediately in response to certain events, actions or triggers, but theAugmented Intelligence System can be programmed to process the data andrespond with a certain delay (e.g., every few minutes, hours, or days).Near-real time processing may be used for data processing or complexevent processing, or a combination thereof. Complex event processing(CEP) combines data from multiple sources to detect patterns and attemptto identify either opportunities or threats. The goal can be to identifysignificant events and respond fast, for example sales leads, orders orcustomer service calls.

Data may be processed using batch processing the system and techniquesdescribed herein. In some embodiments, data processing may be even lesstime sensitive than in methods where near-real time processing may beapplied (e.g., where typically large volumes of data are collected andstored over days or months). Historical Data may be processed and outputat one or more specified times. Batch processing can be an efficient wayof processing high volumes of data where a group of transactions iscollected over a period of time. Data can be collected, entered,processed and then the batch results are produced.

System 100 of FIG. 1 may comprise technologies that may be used by auser, e.g., an agent's supervisor, to intervene, e.g., before, during,or after a call, chat, SMS, email or other communication between, e.g.,a customer and an agent. In some embodiments, AIS 300 may interface witha screen displayed on the agent's computer or telephone device. Thatscreen may display (e.g., in real time), a (graphical) analysis of callcenter stressors, threshold for resilience factors, and/or one or moremetrics for Emotional Exhaustion. In addition the metrics may be used totrigger one or more interventional measures across different modalitiessuch as visual, audio and/or haptics feedback. By way of non-limitingexamples, visual interventional measures may comprise an agent receivingan uplifting or reassuring notification on their computer screen liveduring a difficult customer interaction (e.g., over the phone). An audiointerventional measure may comprise listening to soothing music (betweencalls) for individual stress-reducing technique or listening tosimulated supervisor audio instructions before, during or after calls.Similarly, haptic interventional measures may comprise smart phone orsmart watch alert as a reminder to take more frequent and/or differenttimed breaks. Still other interventional measures are possible such asthe agent being routed easier calls for a specific period of time or theagent receiving incentives/rewards as part of one or more gamificationelements to improve morale and performance.

A supervisor's dashboard may display (e.g., in real time), a (graphical)analysis of, e.g., call center stressors, threshold for resiliencefactors, a metric for emotional exhaustion, and/or notification forstress-reducing organizational change. Using individual emotionalexhaustion metrics for each agent, AIS 300 may compile anonymized teammetrics for supervisors to review and take action accordingly. Forexample, through personalized training and coaching, revised incentivesand/or team assignments, revised job policies to empower agents or toprovide more autonomy over break times and/or to offer incentives toirate customers etc.

An example AIS 300 is shown in FIG. 3. In some embodiments, augmentedintelligence system (AIS) 300 receives and/or transmits data from or toan IPS and/or routing engine. In some embodiments, augmentedintelligence system (AIS) 300 comprises one or more processors and/orinstructions. The instructions, when executed by one or more processorsperform one or more methods, such as those described herein. AIS 300 mayinclude an application programming interface, a user interface, a dataanalytics module (e.g., one or more data analytics engines), a dataprocessing module, a reporting module, an event processing module, amodeling module, and/or an authentication module.

A model as described herein may utilize one or more data inputs forcalculating an emotional exhaustion measure, e.g., for a user, e.g., acustomer. In some embodiments, a data input can be or comprisehistorical and/or archived data, e.g., customerinteraction/communication history (e,g., obtained from data associatedwith or obtained from CRM)), previous interaction counts, complexity,CSAT/survey data, and/or history with different agents/teams. In someembodiments, a data input can be or comprise streaming data, e.g., webclickstreams measured during the interaction that is being routed; wordsspoken, heard and/or written during the current communication request asit is being routed to the agent, behavioral signals from the currentcommunication request as it being routed as a measure of emotionalexhaustion, mobile/sensor/geolocation/time of day/background noise todetermine cognitive load/decision fatigue at the time of theinteraction, and/or Social Media/News Feed (e.g. related to recent localevents).

In some embodiments, a user's emotional exhaustion metric mayincorporate background noise, such as a crying baby, dog barking, etc.,to determine their emotional exhaustion levels. If the requestor'semotional exhaustion levels past a predetermined threshold, based uponthe background noise, the AIS 300 may provide guidance (e.g., throughinterventions and/or notifications) to the agent in order to lower therequestor's emotional exhaustion levels. For example, a requestor'semotional exhaustion levels may be below a predetermined level but whena baby is heard crying, their emotional exhaustion passes thepredetermined threshold. In that scenario, the AIS 300 may provide avisual notification on the agent's computer interface suggesting to theagent to speak a bit slower than normal and to offer the requestor toplace the agent on speaker so the requestor can hold the baby during thecommunication. Similarly, if an agent's emotional exhaustion metric iscomputed to be higher than a pre-determined threshold due to thebackground noise in the contact center, a routing engine may not routerelatively more difficult communication requests to that agent where therequestor's emotional exhaustion metric is also computed to berelatively higher than normal.

Examples for data inputs relating to emotional exhaustion of a user(e.g, a customer) include words spoken by agents during currentinteraction vs. historical trends, agent aggression/rudeness, Agentbeing unhelpful, customer interaction history, previous interactioncounts, outcomes, complexity, customer effort score (CES) or CSATscores, cognitive load and/or decision fatigue trends. In someembodiments, cognitive load is more of a point in time measure and canbe fixed immediately. In some embodiments, decision fatigue is aresource depletion issue.

Example operational definitions of constructs are shown in Table 1.

TABLE 1 Inputs Operational Definitions Construct 1 Call Center Objectivenegative stressors in the job Stressors environment. Customer verbalaggression Caller yelling at, insulting, cursing at, and/or threateningan agent. Highly demanding customers Caller language indicates a refusalto accept the solutions provided by the agent. (e.g. pushy or talkingover agent) Decreased Job autonomy Agent sounds like they are speakingfrom a script. Increased emotional labor Agent is continuallyreadjusting and coordinating their effort to deal with the caller byremaining calm, actively listening, increasing patience, and showingempathy. Increased time pressure because of Agent feels pressured tocomplete a call productivity metrics quickly. Increased performancemonitoring Pressure to achieve organizational goals for service &rapport between agents and callers (measured by customer satisfactionand contact quality). Construct 2 Stress Agent's personal experiencesand cognitive-behavioral reactions to call center stressors. Age Agent'schronological age. Tenure Agent's total length of time in current role.Emotion regulation Agent's cognitive appraisals and coping responses tocall center stressors (e.g. faking positive emotions or perspectivetaking) Perception of control at work Agent's perception of monitoringtools used by client supervisors and client executives.Depersonalization Agent disengages from their work and become uncaringand/or cynical towards callers. Construct 3 Emotional Agent's feeling ofbeing emotionally Exhaustion drained by work and lacking the energy tomaintain work effort. Construct 4 Frequent Absences Hours lost at workAgent's time (in hours) spent out of the office that is not related toplanned vacation time. Avoidance of tasks Agent's adherence to schedule.Construct 5 Employee Chum Rate (by quarter) of agents who left theclient organization and require replacement by new hires. VoluntaryTermination Agent leaves for better opportunity. Involuntary TerminationAgent is let go because of poor performance.

Data that may be used with a technology or model as described herein mayinclude one or more behavioral signals, metrics or factors, measures ofpace over a time period. As such, a “speaking rate” at which the firstparty has spoken and a measure of pace at which the second party hasspoken may be identified and graphically charted together forcomparison. Analytical tools of the present disclosure may identify risea measure of tone or a vocal “dynamic variation”. Such tonal identifyinganalytical tools may identify that measures of tone associated with afirst person and with a second person may be graphically chartedtogether for comparison. Such comparisons may span measurements madeover an interval of time (seconds or minutes, for example). Suchmeasures of time may be associated with a time interval, such as theprevious minute, the previous 2 minutes, the previous 3 minutes, orsince the beginning of the communication. Furthermore a running“instantaneous” measure over a shorter preceding period of time may alsobe collected and analyzed. Such shorter periods of time may correspondto a time period no greater than 10 seconds, 5 seconds, 3 seconds, 1second, 0.5 second, 0.3 second, 0.2 second, or 0.1 second.

Other forms of data analysis and collection may be related measures ofvocal effort with which the first party has spoken and a measure ofvocal effort with which the second party has spoken, degrees ofarticulation (e.g., “articulation space”) with which the first party hasspoken and a measure of degree of articulation with which the secondparty has spoken, measures of amounts of time the first party has spokenrelative to the second party (e.g., “speaking participation” or“engagement”), or a measure of conversational engagement of the partiesover an interval of time. As mention above, such analysis and datacollection may be performed over smaller or longer periods of time. Anyanalytics collected or calculated may be charted in charts when makingcomparisons in performance between different agents, for example.

Models consistent with the present disclosure may use one or more datainputs for calculating metrics. Such metrics may be related an amount ofeffort expended by a requestor, customer or agent. In some instances,received data can include historical data or archived information. Suchhistorical data or archived information may include a customer effortscore, customer survey data, information collected from one or moreprevious interactions with a same customer, a number of interactions, oran amount of time spent trying to accomplish a goal. Such models may beused to characterize data associated with or obtained from a customerrelationship management (CRM) technology that may cross referenceinformation received over time from different types of communicationchannels and that may process historical data from those different typesof communication channel regarding behavior patterns of a same customeror agent. These models may parse words spoken or written during previousinteractions. In some embodiments, a data input can include streamingdata (e.g., sequences of questions and answers, web click sequences,words spoken or written during the evaluation of a current communicationrequest, or include behavioral signals from a current communication.Furthermore, such models may be used when request are currently beingrouted by according to the present disclosure.

A model as described herein may utilize one or more data inputs forcalculating a metric. For example, a metric for effort required by acustomer or an agent may be computed using factors that include a numberof additional steps remaining to accomplish a goal for a communicationrequest, an amount of information communicated, or an amount of time.For example, an amount of time it typically takes to complete a task ofsimilar complexity. These models may utilize one or more data inputs forused to calculate a level of emotional exhaustion and they may includefactors that relate to: historical data, achieved data, agentavailability, agent utilization, amount of user time off between calls,a number of interaction, types of interactions, context, complexity,interaction outcome or resolution, hours logged, a number of wordscommunicated (spoken, heard, or written) during interactions; webclick-stream information, behavioral signal history (from past audiointeractions), and/or human resource data (e.g., agent profile, skilllevel, gender, language, accent, job policies, training/coaching,incentive or reward structure, goals, performance metrics). In someembodiments, a data input can include streaming data, such as voicedata, voice behavioral data, web/app clickstream data, wordscommunicated (heard, written and/or spoken words as compared tohistorical trends), number of application screens open at the time of acommunication, a number of performance monitoring tools deployed on anagent device, an ability to collaborate with other agents, a frequencyof collaboration with other agents, or such data inputs may includesocial media/news feed data that may be related to recent local events.

FIG. 4 illustrates a communication network that may be used to sendcommunications between different computing devices when methodsconsistent with the present disclosure are performed. FIG. 4 shows anillustrative network environment 400 consistent with the methods andsystems described herein. FIG. 4 is a block diagram of an exemplarycloud computing environment 400. The cloud computing environment 400 mayinclude one or more resource providers 402 a, 402 b, 402 c(collectively, 402). Each resource provider 402 may include computingresources. In some implementations, computing resources may include anyhardware and/or software used to process data. For example, computingresources may include hardware and/or software capable of executingalgorithms, computer programs, and/or computer applications. In someimplementations, example computing resources may include applicationservers and/or databases with storage and retrieval capabilities. Eachresource provider 402 may be connected to any other resource provider402 in the cloud computing environment 400. In some implementations, theresource providers 402 may be connected over a computer network 408.Each resource provider 402 may be connected to one or more computingdevice 404 a, 404 b, 404 c (collectively, 404), over the computernetwork 408.

The cloud computing environment 400 may include a resource manager 406.The resource manager 406 may be connected to the resource providers 402and the computing devices 404 over the computer network 408. In someimplementations, the resource manager 406 may facilitate the provisionof computing resources by one or more resource providers 402 to one ormore computing devices 404. The resource manager 406 may receive arequest for a computing resource from a particular computing device 404.The resource manager 406 may identify one or more resource providers 402capable of providing the computing resource requested by the computingdevice 404. The resource manager 406 may select a resource provider 402to provide the computing resource. The resource manager 406 mayfacilitate a connection between the resource provider 402 and aparticular computing device 404. In some implementations, the resourcemanager 406 may establish a connection between a particular resourceprovider 402 and a particular computing device 404. In someimplementations, the resource manager 406 may redirect a particularcomputing device 404 to a particular resource provider 402 with therequested computing resource.

Resource provider 402 may each be a human agent or at least some ofresource provider 402 could include computation engines or forms ofaugmented intelligence discussed in respect to FIGS. 1-3.

FIG. 5 illustrates exemplary computing devices that may be used toimplement methods consistent with the present disclosure. FIG. 5 showsan example of a computing device 500 and a mobile computing device 550that can be used implement method consistent with the presentdisclosure. The computing device 500 is intended to represent variousforms of digital computers, such as laptops, desktops, workstations,personal digital assistants, servers, blade servers, mainframes, andother appropriate computers. The mobile computing device 550 is intendedto represent various forms of mobile devices, such as personal digitalassistants, cellular telephones, smart-phones, and other similarcomputing devices. The components shown here, their connections andrelationships, and their functions, are meant to be examples only, andare not meant to be limiting.

The computing device 500 includes a processor 502, a memory 504, astorage device 506, a high-speed interface 508 connecting to the memory504 and multiple high-speed expansion ports 510, and a low-speedinterface 512 connecting to a low-speed expansion port 514 and thestorage device 506. Each of the processor 502, the memory 504, thestorage device 506, the high-speed interface 508, the high-speedexpansion ports 510, and the low-speed interface 512, are interconnectedusing various busses, and may be mounted on a common motherboard or inother manners as appropriate. The processor 502 can process instructionsfor execution within the computing device 500, including instructionsstored in the memory 504 or on the storage device 506 to displaygraphical information for a GUI on an external input/output device, suchas a display 516 coupled to the high-speed interface 508. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices may be connected, with each device providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system).

The memory 504 stores information within the computing device 500. Insome implementations, the memory 504 is a volatile memory unit or units.In some implementations, the memory 504 is a non-volatile memory unit orunits. The memory 504 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 506 is capable of providing mass storage for thecomputing device 500. In some implementations, the storage device 506may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. Instructions can be stored in an information carrier.The instructions, when executed by one or more processing devices (forexample, processor 502), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices such as computer-or machine-readable mediums (forexample, the memory 504, the storage device 506, or memory on theprocessor 502).

The high-speed interface 508 manages bandwidth-intensive operations forthe computing device 500, while the low-speed interface 512 manageslower bandwidth-intensive operations. Such allocation of functions is anexample only. In some implementations, the high-speed interface 508 iscoupled to the memory 504, the display 516 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 510,which may accept various expansion cards (not shown). In theimplementation, the low-speed interface 512 is coupled to the storagedevice 506 and the low-speed expansion port 514. The low-speed expansionport 514, which may include various communication ports (e.g., USB,Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,or a networking device such as a switch or router, e.g., through anetwork adapter.

The computing device 500 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 520, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 522. It may also be implemented as part of a rack server system524. Alternatively, components from the computing device 500 may becombined with other components in a mobile device (not shown), such as amobile computing device 550. Each of such devices may contain one ormore of the computing device 500 and the mobile computing device 550,and an entire system may be made up of multiple computing devicescommunicating with each other.

The mobile computing device 550 includes a processor 552, a memory 564,an input/output device such as a display 554, a communication interface566, and a transceiver 568, among other components. The mobile computingdevice 550 may also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 552, the memory 564, the display 554, the communicationinterface 566, and the transceiver 568, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 552 can execute instructions within the mobile computingdevice 550, including instructions stored in the memory 564. Theprocessor 552 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 552may provide, for example, for coordination of the other components ofthe mobile computing device 550, such as control of user interfaces,applications run by the mobile computing device 550, and wirelesscommunication by the mobile computing device 550.

The processor 552 may communicate with a user through a controlinterface 558 and a display interface 556 coupled to the display 554.The display 554 may be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface556 may comprise appropriate circuitry for driving the display 554 topresent graphical and other information to a user. The control interface558 may receive commands from a user and convert them for submission tothe processor 552. In addition, an external interface 562 may providecommunication with the processor 552, so as to enable near areacommunication of the mobile computing device 550 with other devices. Theexternal interface 562 may provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces may also be used.

The memory 564 stores information within the mobile computing device550. The memory 564 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 574 may also beprovided and connected to the mobile computing device 550 through anexpansion interface 572, which may include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 574 mayprovide extra storage space for the mobile computing device 550, or mayalso store applications or other information for the mobile computingdevice 550. Specifically, the expansion memory 574 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 574 may be provided as a security module for the mobilecomputing device 550, and may be programmed with instructions thatpermit secure use of the mobile computing device 550. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, instructions are stored in an information carrier and,when executed by one or more processing devices (for example, processor552), perform one or more methods, such as those described above. Theinstructions can also be stored by one or more storage devices, such asone or more computer- or machine-readable mediums (for example, thememory 564, the expansion memory 574, or memory on the processor 552).In some implementations, the instructions can be received in apropagated signal, for example, over the transceiver 568 or the externalinterface 562.

The mobile computing device 550 may communicate wirelessly through thecommunication interface 566, which may include digital signal processingcircuitry when necessary. The communication interface 566 may providefor communications under various modes or protocols, such as GSM voicecalls (Global System for Mobile communications), SMS (Short MessageService), EMS (Enhanced Messaging Service), or MMS messaging (MultimediaMessaging Service), CDMA (code division multiple access), TDMA (timedivision multiple access), PDC (Personal Digital Cellular), WCDMA(Wideband Code Division Multiple Access), CDMA2000, or GPRS (GeneralPacket Radio Service), among others. Such communication may occur, forexample, through the transceiver 1068 using a radio-frequency. Inaddition, short-range communication may occur, such as using aBluetooth®, Wi-Fi™, or other such transceiver (not shown). In addition,a GPS (Global Positioning System) receiver module 570 may provideadditional navigation- and location-related wireless data to the mobilecomputing device 550, which may be used as appropriate by applicationsrunning on the mobile computing device 550.

The mobile computing device 550 may also communicate audibly using anaudio codec 560, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 560 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 550. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 550.

The mobile computing device 550 may be implemented in a number ofdifferent forms, as shown in the figure. For example, it may beimplemented as a cellular telephone. It may also be implemented as partof a smart-phone, personal digital assistant, or other similar mobiledevice.

FIG. 6 illustrates a set of exemplary steps that may be performed bylearning module software consistent with the present disclosure. Oneskilled in the art will appreciate that, for this and other processesand methods disclosed herein, the functions performed in the processesand methods may be implemented in differing order. Furthermore, theoutlined steps and operations are only provided as examples, and some ofthe steps and operations may be optional, combined into fewer steps andoperations, or expanded into additional steps and operations withoutdetracting from the essence of the disclosed embodiments

The steps of FIG. 6 may be initiated by an augmented/artificialintelligent (AI) system consistent the present disclosure. As such, AIsystem 300 of FIG. 3 may cause a processor to execute instructions outof a memory to perform the steps illustrated in FIG. 6. In step 605 ofFIG. 6 a processor executing instructions out of a memory may searchdata stored in a learning database when a first set of data isretrieved. This first set of data may be data associated with a firstagent that works at a call center and may include the name or age of thefirst agent, emotional data, behavior data, skill level information,speech frequency data, or other data related to the first agent. Speechfrequency data, for example, may be used to identify how quickly thefirst agent speaks may be retrieved in step 605. This speech frequencydata may be used to set a historical benchmark or to identify historicalmetrics associated with the first agent's previous interactions withother agents or customers. Other data may also be retrieved in step 605,such as data identifying that the average frequency range for humanspeech varies from 80 to 260 Hertz, he vocal speech frequency of anadult male ranges from 85 to 180 Hertz, while the frequency of an adultfemale ranges from 165 to 255 Hertz. Step 610 of FIG. 6 may then filterthe retrieved set of first agent data. Step 615 may then select a firstparameter that may be used in a correlation analysis. Filtering step 610may filter the retrieved data for the age of the first agent and anevent time may be selected in step 615 of FIG. 6, for example.

Correlations may then be performed for all different sets of parametricdata in step 620 of FIG. 6. These correlations, combined with the firstparameter may be used to identify whether a certain parameter maycorrespond to other historical at least to a threshold amount. Step 625of FIG. 6 may then identify whether a certain correlation matches to apredetermined threshold level of >95%. When such a correlation meetsthis threshold, parameters associated with the correlation may beconsidered relevant to the first agent, for example if agents with thesame age, event time, or other matching factor usually have a highspeaking pace, 200 words per minute in the first 5 minutes of a call,the first agent may be experiencing emotional exhaustion. The correlateddata point may then be extracted in step 630 and then stored in adatabase in step 635 of FIG. 6.

Program flow may flow to step 640 from directly from step 625 when step625 identifies that a particular parameter analyzed does not meet thecorrelation threshold of >95%. Program flow may also move from to step640 after step 635. Step 640 is a determination step that may identifywhether any parameters remain in a set of retrieved data, when yesprogram flow may move to step 645 where a next parameter is selected,and then program flow may move to step 620 where an additionalcorrelation may be performed. When step 640 identifies that noadditional parameters remain, program flow may move to step 650 that mayinform an AI system that the AI system may perform another function.

FIG. 7 illustrates how data may be compiled and used by a machinelearning module consistent with the present disclosure. One skilled inthe art will appreciate that, for this and other processes and methodsdisclosed herein, the functions performed in the processes and methodsmay be implemented in differing order. Furthermore, the outlined stepsand operations are only provided as examples, and some of the steps andoperations may be optional, combined into fewer steps and operations, orexpanded into additional steps and operations without detracting fromthe essence of the disclosed embodiments. The data of FIG. 7 may havebeen collected or tabulated when an agent was engaged in an discussionbetween an agent a customer when that agent responded to a request fromthe customer.

FIG. 7 includes various charts, the charts included in FIG. 7 includepolite words and phrases versus an event time, times that the phrase“Thank You” was said versus event time, speaking pace versus event time,and audio waveform versus event time. A portion of FIG. 7, identified as“word analysis” is filtered by the event time and finding the variouscorrelations within the first 5 minutes of an event. Various parameterssuch as polite words used per minute, the number of “Thank You's” saidare illustrated in charts included in FIG. 7. Note that the polite wordsversus event time chart include time in a horizontal axis and a measureof polite words per minute in a vertical axis of the chart. The data inthe polite words versus event time chart may be processed using acorrelation analysis. When such an analysis identifies thatnon-correlated parameters with the event time is the number of politewords said has only a 15% correlating (which is below the 95%threshold), there may be a finding that there is no correlation betweenpolite words and event time. In such an instance no data points would bestored in a database based on the meager 15% correlation.

Other charts in FIG. 7 are identified as “audio analysis.” In thesecharts the data that is filtered by the event time and finding thevarious correlations within the first 5 minutes of an event. Herevarious parameters include as speaking pace and waveform frequency. Hereagain an correlation analysis may be performed, when such an analysisidentifies that the event time and speaking pace match with a 96%correlating (above the 95% threshold), there may be a finding that thereis a correlation between an agents speaking rate increasing in the first5 minutes of the event. Such a correlation may indicate that an agent isemotionally exhausted and that data may be stored in the Database to beused as a reference for future events. The most re-occurring data point(for example the most re-occurring data point is a speaking rate of 200words per minute 5 minutes into the event) is extracted and is stored inthe database to be used for future reference.

FIG. 8 includes a table of data that associates data associated withdifferent agents with parametric data collected over a span of time. Thedata in the table of FIG. 8 cross references agent identifiers, agentage, types of data, time, word pace per minute, audio waveformfrequency, a measure of polite word, and a number of time the phrase“Thank You” was spoken during the span of an event. The table of FIG. 8may include data charted in the charts of FIG. 7. Note that each of theages of the agents in FIG. 8 is 25 and that a type of data analyzed wasaudio data. Note also that speaking pace of these agents varied between160 and 180 words per minute with speaking wave form frequencies varyingfrom 225 Hertz (Hz) to 260 Hertz (Hz). One skilled in the art willappreciate that, for this and other processes and methods disclosedherein, the functions performed in the processes and methods may beimplemented in differing order. Furthermore, the outlined steps andoperations are only provided as examples, and some of the steps andoperations may be optional, combined into fewer steps and operations, orexpanded into additional steps and operations without detracting fromthe essence of the disclosed embodiments.

FIG. 8 displays data that may be stored in a Machine Learning Database.Such a Machine Learning Database may display the agent data that iscollected through previous events in which an agent has been deemed tohave experience emotional exhaustion, for example. Such stored data maybe audio, video, or text data that has been collected through operationof an AI system, such as AIS 300 of FIG. 3. This database contains theagent ID, agent age, type of data and for this example, event time, pacewhich is the speaking pace of the agent, frequency which is the waveformfrequency of the agent, polite words which is the amount of polite wordsused by the agent, and “Thank You's” which is the amount of times thatthe agent said “Thank You”. It should be noted that the above example ofthe Machine Learning Module may have a various data inputs that havebeen previously mentioned. Also, the results of various historicalanalyses, also previously mentioned, may be used as data inputs for theMachine Learning Module. Below are examples of some of the historicalanalyses that may be performed as reviewed in the following examples

Example 1: An example of a historical analysis of data may be a trendanalysis on customer aggression data. For example, customer aggressionmay be determined by comparing historical, real-time or streaming audioor text data to a Customer Aggression database of Table 2, illustratedbelow. Table 2 shows examples of aggression categories such as yell,insult, curse (like swear words), and threat. An aggression level may beidentified by way of a calculation that determines the number ofaggressive acts. The identifiers for a yell may be if the waveform ormetadata of a waveform or some “mathematical operation of the waveform”(e.g. the number of time units of audio above a certain amplitude), fromthe audio data, frequency increases, if the waveform amplitudeincreases, or if the waveform quality decreases, to name a few.

TABLE 2 Customer Aggression Database Aggression Yell Insult Curse ThreatLevel Waveform Frequency Dumb Curse Word 3 Increases 1 WaveformAmplitude Idiot Curse Word Or else 4 Increases 2 Curse Word Just wait 23 . . . . . . . . . . . . 4

A historical analysis of data may include a trend analysis on customeraggression data. In such an instance, customer aggression may bedetermined by comparing historical data used to identify aggressive actsthat may be associated with an aggression category. When an aggressiveaction is identified during an event, the category of that act may beextracted, along with a time stamp. This information may be plotted on atrend analysis graph as shown in FIG. 9. In the graph of FIG. 9 they-axis shows the number of aggression levels or acts by time. The x-axisof FIG. 9 shows the number of minutes. If a predetermined threshold isreached, i.e. if three out of five categories are identified in a 5minute period, then it can be concluded that the customer is showingsigns of aggression due to an increasing trend of showing aggressiontowards the agent. However, if the predetermined threshold is notreached and there only 1 or two aggression levels identified over a 25minute time period it can be determined that the customer is not showingan increasing trend of aggression towards the agent.

Example 2: Another example of historical analysis of data may be acohort analysis on the depersonalization of an agent. For example, whenan agent begins to disengage or stop caring about their work performancethere may specific words or phrases that can be collected through audioand/text data from historical archives (words or phrases that areuncaring, or cynical, etc. For each of these words of phrases found, adisengaging level may be calculated, in real-time. Such calculations maybe based on data extracted from a stream of data. These words or phrasesand the calculation of disengaging level may be stored in adepersonalization database (Table 3) to be compared with the audioand/or text data. Table 3 displays the depersonalization database thatcontains categories and words, or phrases related to the categories, forexample the uncaring category may contain phrases such as, can yourepeat that, I don't understand, and I forgot/forget, to name a few. Itshould be noted that agent actions may also be incorporated into thedatabase such as arriving late to work, taking extending breaks orhanging up on customers.

TABLE 3 Depersonalization Database Uncaring Cynical Disengaging levelCan you repeat that? I didn't say that 2 I don't understand . . . 1 . .. . . . . . . Uncaring word/phrase Cynical word/phrase — N N

When a category is identified it may be extracted and stored in adatabase that tracks the amount of depersonalization actions performedby an agent over a predetermined amount of time, for example by event.These identified depersonalization actions may be analyzed by using acohort analysis (Table 4). Table 4 shows the cohort analysis of a seriesof events for an agent, the analysis shows the event number, the numberof depersonalization actions during the call, and the events followingthe event number. For example, Event 1 had 3 depersonalization actions,in the following call (event 2) only 66.6% of the depersonalizationactions re-occurred, however in the third, fourth, and fifth followingcalls 100% of those depersonalization actions re-occurred.

TABLE 4 Depersonalization Events Event # Actions 1 2 3 4 5 1 3 66.6%33.3% 100.0% 100.0% 100.0% 2 2 50.0% 100.0% 100.0% 100.0% 3 1 100.0%100.0% 100.0% 4 4 100.0% 100.0% 5 6 100.0% 6 8

This analysis can show whether the agent is continuously usingdepersonalization actions during events, as well as determine if newactions are occurring through a series of calls. If the percentages areincreasing or remain over a predetermined percentage, for example over85%, then it can be concluded that the agent is disengaged from theirwork and is experiencing emotional exhaustion. In Table 4, this 85%threshold is exceeded for three straight calls and this data may be sentto a supervisor display with a recommendation to relieve the agent for ashort break or if the actions continue send the agent home for the day.These types of analysis may also be performed over a longer period oftime, for example three months, in order determine if an agent is slowlybecoming disengaged from the work due to depersonalization. If this isthe case a recommendation may be sent to a supervisor to suggest to theagent to take some vacation time, see a doctor or therapists, etc.

Example 3: Another example of historical analysis of data may be using aSWOT analysis on employee churn. For example, when an employee isterminated from the company either voluntary, e.g., for a betteropportunity, or involuntary, e.g., poor performance, they can be givenan exit survey. This survey (Termination Survey, e.g., as shown in Table5) may be directed to determine potential stress activators foremployees which may lead to the employee being terminated. Table 5 showsa potential example of the termination survey in which there are surveyquestions and choices to be selected by the employee, the choices aredifferent depending on how the employee was terminated. For example, onesurvey question may be “what was the biggest cause of stress on calls?”and the choices for the voluntary terminated employee may be “customerverbal aggression, decreased job autonomy, or increased performancemonitoring”. It should be noted that this survey may be structured inmultiple ways in order to collect terminated employee data such as awrite in survey, choices selected from emotional exhaustion results frompreviously terminated employees, etc.

TABLE 5 Survey Questions Voluntary Termination Involuntary TerminationWhat was the Customer Verbal Highly Demanding biggest cause ofAggression Customers stress on calls? Decreased Job Autonomy IncreasedEmotional Increased Performance Labor Monitoring Increased Time PressureWhat was the Age Emotion Regulation biggest self- TenureDepersonalization identified factor for Perception of Control stress?Survey Question N — —

Historical Analysis of data may be stored in a database, such as anenterprise data warehouse system used for reporting and data analysis,and that may be referred to as Big Data analysis. Such analysis mayinclude, yet is not limited to (1) time series analysis, (2) statisticalanalysis, (3) qualitative research data analysis, (4) fundamentalanalysis for forecasting, (5) qualitative comparative analysis, (6) SWOTanalysis, (7) interpretative phenomenological analysis (transcriptions),(8) meta-analysis, (9) specific technical analysis, (10) sociologicalanalysis, (11) comparative historical research analysis, (12) trendanalysis, (13) emerging issues analysis, (14) spatial analysis, (15)numerical analysis, (16) principal component analysis, (17) Linkanalysis is uses to evaluate relationships (connections) between nodes,(18) bioinformatics analysis, (19) scenario analysis, (20) machinelearning analysis, (21) content analysis, (22) data visualizationanalysis, (23) Cohort analysis, (24) multilinear principal componentanalysis, (25) Contrastive analysis (the systematic study of a pair oflanguages with a view to identifying their structural differences andsimilarities, (26) indicator analysis, (27) analysis of variance, (28)Chaos theory analysis, (29) sentiment analysis (sometimes known assentiment analysis or emotion AI) refers to the use of natural languageprocessing, text analysis, computational linguistics, (30) demographicanalysis.

The employee selections are stored in a database and can be analyzedusing a SWOT analysis (e.g., as shown in Table 6), to determineemotional exhaustion of terminated employees. Results of Table 5 may beused to populate Table 6 automatically, in that certain survey answers(e.g. customer verbal aggression in Table 5 results in a weakness inTable 6). The voluntary termination choices may be categorized as“weaknesses” or “threats” since the employee is leaving the company ontheir own decision. It may be concluded from these choices thatemployees that experience an increase in these emotional exhaustion datainputs are more likely to leave the company for other opportunities.This mapping could then be used to create numerical valuesautomatically. Also, the data of Table 5 could be input by using datafrom real time or historical data from the other examples above (trendanalysis, cohort analysis) so in real time a profile of call agents canbe developed. With enough data in the system, the system could predictcall agents that could be involuntary terminated.

TABLE 6 SWOT Analysis Strength Weakness Handling Demanding CustomerVerbal Aggression Customers Decreased Job Autonomy Emotional LaborIncreased Performance Productivity metrics Monitoring Emotion RegulationAge Depersonalization Tenure Perception of Control Opportunity Threat

Results of this historical data analysis may be used as a data input forthe machine learning/AI model described above in order to determine thethreshold in which employees are subjected to these types of events.Conversely, the involuntary termination choices may be categorized as“strengths” or “opportunities” since the employee has been terminateddue to job performance. This shows that the company has baselineexpectation that employees are required to handle certain types ofevents and interactions with the customers. It may be concluded fromthese choices that employees are expected to handle demanding customersand maintain a level of professionalism above a predetermined basethreshold of emotional exhaustion, without exceeding a predeterminedthreshold that would warrant concern. For example, an employee may beexpected to handle at least one highly demanding customer per day. Thisevent would increase the employee's total emotional exhaustion levelsabove the baseline threshold but would not be the same if the employeehandled 10 highly demanding customers in a row. This baseline thresholdmay also be used as an emotional exhaustion data input for the machinelearning/AI model described above to determine if the employee ismaintaining a certain performance level.

In certain instances, an example apparatus consistent with the presentdisclosure may use a blockchain database that contains information aboutemployee perks, such as, days off, better shifts, company swag, giftcards, or cash bonuses, in which the employees may be giventokens/credits/keys to access the employee perks. The blockchaindatabases may include customer or requestor perks information, such as,awards, cash or discounts on company's products and/or services, thirdparty vendor products and/or services, move up higher in a priority ofqueue of pending requests, or request to be routed to a live agentimmediately. The blockchain database may send the private and publickeys to the AI system to be stored and/or processed by the AI system.The AI system may send the public keys to the users (e.g., agents,supervisors, requestors, or customers) based upon one or more factorsassociated with a communication request. The users may send the publickey to the AI system to be combined to with the corresponding privatekey in order to access the perk in the blockchain database. The perk,once unlocked by the use of the private and public keys, is sent to theuser to be used as they desire.

One or more agents may receive employee perks through public keys fromthe AIS 300 of FIG. 3, these may be used for achieving specificoperational or performance goals. For example, agents may receive publickeys in response to positive feedback surveys if the requestor's goalwas met satisfactorily by those agents. The agents may also opt-in orindicate their willingness to process more difficult (e.g., based on therequestor's relatively high emotional exhaustion measure) communicationrequests that would otherwise not be routed to them by a routing engineand receive public keys from other agents to whom that request was orwould otherwise have been routed instead. In other embodiments, agentsmay also be rewarded public keys based on emotional exhaustion measuressuch that if the requestor's emotional exhaustion measure drops by acertain percentage or below specific threshold during a communicationrequest, then the agent will be awarded with a public key associatedwith an employee perk. Still other embodiments of awarding public keysto users are possible.

Alternatively or additionally, customers or requestors may receive perksthrough public keys from a AI system. For example, a customers orrequestor may receive one or more public keys as a remediation effortafter their communication request has been handled poorly by an agent.This, in turn, may be determined by analyzing the customer's orrequestor's emotional exhaustion metric level rises above a specificthreshold during the processing of a communication request by an agent.In that scenario, a customer may use the public key to access one ormore customer perks in the blockchain database.

Note that an AI system may include or otherwise be communicativelycoupled to an AI system portal that can be used by users (e.g., agents,supervisors, or customers) to interact with other users or members. ThisAIS Portal may be an online community site that allows members tomanage, track and redeem the perks they have acquired through combiningtheir public key with the private key, from the AIS 300, to access theperks on the blockchain database. The AIS Portal may have asecurity/privacy level that forces the member to sign into the AISPortal with the use of a user name/password combination, securityquestions, biometric, etc. in order to input their acquired public keyto be combined with the corresponding private key to access theblockchain database. Once logged into the AIS Portal the member may alsotrade or acquire other member's public keys by completing certainactions.

FIG. 10 illustrates an exemplary chart of program flow that may beimplemented to calculate rewards that may be provided to an agentworking at a call center. FIG. 10 includes step 1000, where compensationcalculation software may be initiated. After step 1000, program flowmoves to step 1010, where a ledger that tracks Block chains may beaccessed. After step 1010, information relating to a number of customerreferrals or customer activities may be accessed. These referrals oractivities may be associated with acts performed by an agent.Determination step 1020 may then identify whether a referral or activityattributed to an employee is listed in a trigger list, when no, programflow moves to determination step 1050. Determination step 1050 mayidentify whether additional events relating to the agent should bereviewed, when yes program flow moves back to determination step 1020.When determination step 1050 indicates that there are not any otheragent events to be reviewed, the program flow ends in step 1055 of FIG.10.

When step 1020 identifies that an agent referrals or activity isincluded in a trigger list program flow moves to step 1025 wherereferral and activity data are extracted, next in step 1030 rules usedto encrypt compensation information may be accessed. After step 1030,step 1035 identifies a disbursement rule, step 1040 may identify rewardsthat should be distributed to the agent and those identified rewards maybe distributed to the agent, using a Block chain payment, for example.After step 1045, program flow moves to back to step 1050.

The features and advantages of the present disclosure will become moreapparent from the detailed description set forth below when taken inconjunction with the drawings, in which like reference charactersidentify corresponding elements throughout. In the drawings, likereference numbers generally indicate identical, functionally similar,and/or structurally similar elements.

Throughout the description, where articles, devices, and systems aredescribed as having, including, or comprising specific components, orwhere processes and methods are described as having, including, orcomprising specific steps, it should be understood that, additionally,there are articles, devices, and systems of the present invention thatconsist essentially of, or consist of, the recited components, and thatthere are processes and methods according to the present invention thatconsist essentially of, or consist of, the recited processing steps. Anorder of steps or order for performing actions is immaterial so long asthe invention remains operable. Moreover, two or more steps or actionsmay be conducted simultaneously.

The mention herein of any publication or patent application, forexample, in the Background section, is not an admission that suchpublication or patent application constitutes prior art with respect toany of the claims or subject matter presented herein. The Backgroundsection is presented for purposes of clarity and is not intended to be adescription of prior art with respect to any claim.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations. Thesevarious implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device. Thesecomputer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term machine-readable signal refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (LAN), a wide area network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

OTHER EMBODIMENTS

Embodiment 1: A method for routing communications, the methodcomprising: receiving a request from a computing device associated witha requestor that is associated with a first type of communicationchannel; receiving information included in a set of communications withthe requestor computing device; calculating an emotional exhaustionscore, the calculation based on the received information included in theset of communications; identifying that the emotional exhaustion scorehas at least met an emotional exhaustion threshold; and initiating acorrective action based on the emotional exhaustion score at leastmeeting the exhaustions threshold.

Embodiment 2: The method of Embodiment 1, wherein the corrective actionincludes routing communications from the requestor user device to acomputing device associated with a human agent and the method furthercomprises collecting communication information associated withcommunications between the requestor computing device and the humanagent computing device.

Embodiment 3: The method of any of Embodiments 1-2, further comprisingcalculating an emotional exhaustion score associated with the humanagent.

Embodiment 4: The method of any of Embodiments 1-3, further comprisingidentifying that the human agent emotional exhaustion score has crosseda threshold associated with the human agent.

Embodiment 5: The method of any of Embodiments 1-4, further comprisingsending advice messages to the human agent computing device.

Embodiment 6: The method of any of Embodiments 1-5, further comprisingidentifying that the performance of the human agent is consistent withan event in a reward trigger list, wherein the human agent is providedwith the reward based on the identification that the performance of thehuman agent is consistent with the event.

Embodiment 7: The method of any of Embodiments 1-6, further comprising:identifying that the performance of the human agent is consistent with acorrelation threshold related to a human performance factor; and storinginformation associated with the performance of the human agent in adatabase based on the identification that the performance of the humanagent is consistent with the correlation threshold.

Embodiment 8. A non-transitory computer-readable storage medium havingembodied thereon a program executable by a processor for routingcommunications, the method comprising: receiving a request from acomputing device associated with a requestor that is associated with afirst type of communication channel; receiving information included in aset of communications with the requestor computing device; calculatingan emotional exhaustion score, the calculation based on the receivedinformation included in the set of communications; identifying that theemotional exhaustion score has at least met an emotional exhaustionthreshold; and initiating a corrective action based on the emotionalexhaustion score at least meeting the exhaustions threshold.

Embodiment 9: The non-transitory computer-readable storage medium ofEmbodiment 8, wherein the corrective action includes routingcommunications from the requestor user device to a computing deviceassociated with a human agent and the program is further executable tocollect communication information associated with communications betweenthe requestor computing device and the human agent computing device.

Embodiment 10: The non-transitory computer-readable storage medium ofany of Embodiments 8-9, wherein the program is further executable tocalculate an emotional exhaustion score associated with the human agent.

Embodiment 11: The non-transitory computer-readable storage medium ofany of Embodiments 8-10, wherein the program is further executable toidentify that the human agent emotional exhaustion score has crossed athreshold associated with the human agent.

Embodiment 12: The non-transitory computer-readable storage medium ofany of Embodiments 8-11, wherein the program is further executable tosend advice messages to the human agent computing device.

Embodiment 13: The non-transitory computer-readable storage medium ofany of Embodiments 8-12, wherein the program is further executable toidentify that the performance of the human agent is consistent with anevent in a reward trigger list, wherein the human agent is provided withthe reward based on the identification that the performance of the humanagent is consistent with the event.

Embodiment 14: The non-transitory computer-readable storage medium ofany of Embodiments 8-13, wherein the program is further executable to:identify that the performance of the human agent is consistent with acorrelation threshold related to a human performance factor; and storeinformation associated with the performance of the human agent in adatabase based on the identification that the performance of the humanagent is consistent with the correlation threshold.

Embodiment 15: An apparatus for routing communications, the apparatuscomprising: a memory; and a processor that: receives a request from acomputing device associated with a requestor that is associated with afirst type of communication channel, receives information included in aset of communications with the requestor computing device, calculates anemotional exhaustion score, the calculation based on the receivedinformation included in the set of communications, identifies that theemotional exhaustion score has at least met an emotional exhaustionthreshold, and initiates a corrective action based on the emotionalexhaustion score at least meeting the exhaustions threshold.

Embodiment 16: The apparatus of Embodiment 15, wherein the correctiveaction includes routing communications from the requestor user device toa computing device associated with a human agent and communicationinformation associated with communications between the requestorcomputing device and the human agent computing device are collected.

Embodiment 17: The apparatus of any of Embodiments 15-16, wherein theprocessor executes instructions out of the memory to calculate anemotional exhaustion score associated with the human agent.

Embodiment 18: The apparatus of any of Embodiments 15-17, wherein theprocessor executes instructions out of the memory to identify that thehuman agent emotional exhaustion score has crossed a thresholdassociated with the human agent.

Embodiment 19: The apparatus of any of Embodiments 15-18, wherein theprocessor executes instructions out of the memory to identify that theperformance of the human agent is consistent with an event in a rewardtrigger list, wherein the human agent is provided with the reward basedon the identification that the performance of the human agent isconsistent with the event.

Embodiment 20: The apparatus of any of Embodiments 15-19, wherein theprocessor executes instructions out of the memory to identify that theperformance of the human agent is consistent with a correlationthreshold related to a human performance factor and the apparatusfurther comprising a database that stores information associated withthe performance of the human agent.

Embodiment 21: A computer-implemented method for routing a communicationrequest initiated by a requestor, comprising: receiving, by a processorof an augmented intelligence system over a network, a transmissionrequest from a routing engine to provide an emotional exhaustion metricfor each of one or more human agents in a contact center that areavailable to process the communication request from the requestor;receiving, by the processor of augmented intelligence system, at leastone of streaming data and historical data associated with any of the oneor more human agents, the communication request and the requestor,operating, by the processor of augmented intelligence system, a dataanalytics engine to calculate the metric for each agent based on any ofthe received streaming data and historical data; wherein, the dataanalytics engine selects and applies an emotional exhaustion model toinfer a probability of each of the human agents being emotionallyexhausted over a specific period of time; and transmitting, by theprocessor of augmented intelligence system, to the routing engine theemotional exhaustion metric for each of the one or more human agents inthe contact center wherein the routing engine uses that information toroute the communication request to any one of (i) one of the humanagents, (ii) an automated interaction component, and (iii) aself-service capability configured to process the communication request.

Embodiment 22: The method of Embodiment 21, wherein the routing engineroutes the communication request to one of the human agents.

Embodiment 23: The method of any of Embodiments 21-22, wherein thestreaming data related to the one or more human agents processing thecommunication request is transmitted to the augmented intelligencesystem and is processed the data analytics engine to update theemotional exhaustion metric for the one or more human agents based onthe streaming data.

What is claimed is:
 1. A method for routing communications, the methodcomprising: receiving a request from a computing device associated witha requestor that is associated with a first type of communicationchannel; receiving information included in a set of communications withthe requestor computing device; calculating an emotional exhaustionscore, the calculation based on the received information included in theset of communications; identifying that the emotional exhaustion scorehas at least met an emotional exhaustion threshold; and initiating acorrective action based on the emotional exhaustion score at leastmeeting the exhaustions threshold.
 2. The method of claim 1, wherein thecorrective action includes routing communications from the requestoruser device to a computing device associated with a human agent and themethod further comprises collecting communication information associatedwith communications between the requestor computing device and the humanagent computing device.
 3. The method of claim 2, further comprisingcalculating an emotional exhaustion score associated with the humanagent.
 4. The method of claim 3, further comprising identifying that thehuman agent emotional exhaustion score has crossed a thresholdassociated with the human agent.
 5. The method of claim 4, furthercomprising sending advice messages to the human agent computing device.6. The method of claim 2, further comprising identifying that theperformance of the human agent is consistent with an event in a rewardtrigger list, wherein the human agent is provided with the reward basedon the identification that the performance of the human agent isconsistent with the event.
 7. The method of claim 2, further comprising:identifying that the performance of the human agent is consistent with acorrelation threshold related to a human performance factor; and storinginformation associated with the performance of the human agent in adatabase based on the identification that the performance of the humanagent is consistent with the correlation threshold.
 8. A non-transitorycomputer-readable storage medium having embodied thereon a programexecutable by a processor for routing communications, the methodcomprising: receiving a request from a computing device associated witha requestor that is associated with a first type of communicationchannel; receiving information included in a set of communications withthe requestor computing device; calculating an emotional exhaustionscore, the calculation based on the received information included in theset of communications; identifying that the emotional exhaustion scorehas at least met an emotional exhaustion threshold; and initiating acorrective action based on the emotional exhaustion score at leastmeeting the exhaustions threshold.
 9. The non-transitorycomputer-readable storage medium of claim 8, wherein the correctiveaction includes routing communications from the requestor user device toa computing device associated with a human agent and the program isfurther executable to collect communication information associated withcommunications between the requestor computing device and the humanagent computing device.
 10. The non-transitory computer-readable storagemedium of claim 9, wherein the program is further executable tocalculate an emotional exhaustion score associated with the human agent.11. The non-transitory computer-readable storage medium of claim 10,wherein the program is further executable to identify that the humanagent emotional exhaustion score has crossed a threshold associated withthe human agent.
 12. The non-transitory computer-readable storage mediumof claim 11, wherein the program is further executable to send advicemessages to the human agent computing device.
 13. The non-transitorycomputer-readable storage medium of claim 9, the program is furtherexecutable to identify that the performance of the human agent isconsistent with an event in a reward trigger list, wherein the humanagent is provided with the reward based on the identification that theperformance of the human agent is consistent with the event.
 14. Thenon-transitory computer-readable storage medium of claim 9, wherein theprogram is further executable to: identify that the performance of thehuman agent is consistent with a correlation threshold related to ahuman performance factor; and store information associated with theperformance of the human agent in a database based on the identificationthat the performance of the human agent is consistent with thecorrelation threshold.
 15. An apparatus for routing communications, theapparatus comprising: a memory; and a processor that: receives a requestfrom a computing device associated with a requestor that is associatedwith a first type of communication channel, receives informationincluded in a set of communications with the requestor computing device,calculates an emotional exhaustion score, the calculation based on thereceived information included in the set of communications, identifiesthat the emotional exhaustion score has at least met an emotionalexhaustion threshold, and initiates a corrective action based on theemotional exhaustion score at least meeting the exhaustions threshold.16. The apparatus of claim 15, wherein the corrective action includesrouting communications from the requestor user device to a computingdevice associated with a human agent and communication informationassociated with communications between the requestor computing deviceand the human agent computing device are collected.
 17. The apparatus ofclaim 16, wherein the processor executes instructions out of the memoryto calculate an emotional exhaustion score associated with the humanagent.
 18. The apparatus of claim 17, wherein the processor executesinstructions out of the memory to identify that the human agentemotional exhaustion score has crossed a threshold associated with thehuman agent.
 19. The apparatus of claim 16, wherein the processorexecutes instructions out of the memory to identify that the performanceof the human agent is consistent with an event in a reward trigger list,wherein the human agent is provided with the reward based on theidentification that the performance of the human agent is consistentwith the event.
 20. The apparatus of claim 16, wherein the processorexecutes instructions out of the memory to identify that the performanceof the human agent is consistent with a correlation threshold related toa human performance factor and the apparatus further comprising adatabase that stores information associated with the performance of thehuman agent.